Krylov Subspace Methods for MRI Coil Sensitivity Profiles

Magnetic Resonance Imaging (MRI) is a cornerstone of modern medicine, offering invaluable insights into the human body. However, traditional MRI scans have often been hampered by lengthy acquisition times, limiting their use in certain clinical situations. Over the past two decades, parallel MRI has emerged as a game-changer, offering significant reductions in scan times. This technology hinges on the critical concept of coil sensitivity profiles, which act as a map for accurately interpreting the complex signals acquired during an MRI scan. The quest for optimal performance in parallel MRI requires precise and efficient coil sensitivity profiles. Inaccurate profiles can distort images and introduce artefacts, compromising the diagnostic value of the scan. Additionally, inefficiently generated profiles can significantly increase the computational burden, further extending scan times. Researchers have explored various techniques to extract reliable coil sensitivity profiles. One promising approach involves inexact inverse iterations, which iteratively refine an initial estimate of the profile until it accurately reflects the underlying data. While effective, this method suffers from a major drawback: the computational expense associated with the inner loop of the iterative process. This translates to longer scan times, potentially negating the benefits of parallel MRI in time-sensitive clinical settings. To overcome this limitation, our research team has investigated the implementation of Krylov subspace methods. These powerful mathematical techniques offer a novel approach to tackling the computational challenge. By exploiting the inherent structure of the problem, Krylov subspace methods effectively reduce the dimensionality of the problem, essentially transforming it into a simpler and less complex form. This significantly reduces the computational workload required to achieve accurate coil sensitivity profiles. To validate the efficacy of our proposed method, we conducted a comprehensive evaluation, meticulously comparing the image quality and phase response obtained using various techniques. The results show that the Krylov subspace method not only generated highly accurate and robust coil sensitivity profiles but also achieved this with significantly reduced computation times compared to the previous inexact inverse iterations. This translates to a remarkable improvement in scan speed, paving the way for wider adoption of parallel MRI in clinical practice. The potential impact of this research extends beyond simply reducing scan times. Faster MRI scans can lead to improved patient comfort, particularly for individuals who experience anxiety or claustrophobia during the procedure. Additionally, the ability to acquire high-quality images in a short time opens doors for new applications of MRI, such as dynamic imaging of rapidly changing processes within the body. Results show that the proposed methods improve our previous inexact inverse iterations, leading to smoother coil sensitivity profiles with fewer structural artefacts. The computation speed of the new methods is 5 × faster than the previous inexact inverse iterations. In conclusion, our research demonstrates the significant potential of Krylov subspace methods in improving the efficiency of clinical parallel MRI. This advancement paves the way for faster, more comfortable, and ultimately, more informative MRI scans, contributing to improved patient care and advancing the field of medical imaging.